Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace: http://hdl.handle.net/10459.1/66824

Combined use of low-cost remote sensing techniques and δ13C to assess bread wheat grain yield under different water and nitrogen conditions
Yousfi, Salima; Gracia-Romero, Adrian; Kellas, Nassim; Kaddour, Mohamed; Chadouli, Ahmed; Karrou, Mohamed; Araus Ortega, José Luis; Serret Molins, M. Dolors
Vegetation indices and canopy temperature are the most usual remote sensing approaches to assess cereal performance. Understanding the relationships of these parameters and yield may help design more e cient strategies to monitor crop performance. We present an evaluation of vegetation indices (derived from RGB images and multispectral data) and water status traits (through the canopy temperature, stomatal conductance and carbon isotopic composition) measured during the reproductive stage for genotype phenotyping in a study of four wheat genotypes growing under di erent water and nitrogen regimes in north Algeria. Di erences among the cultivars were reported through the vegetation indices, but not with the water status traits. Both approximations correlated significantly with grain yield (GY), reporting stronger correlations under support irrigation and N-fertilization than the rainfed or the no N-fertilization conditions. For N-fertilized trials (irrigated or rainfed) water status parameters were the main factors predicting relative GY performance, while in the absence of N-fertilization, the green canopy area (assessed through GGA) was the main factor negatively correlated with GY. Regression models for GY estimation were generated using data from three consecutive growing seasons. The results highlighted the usefulness of vegetation indices derived from RGB images predicting GY. This study was supported in part by the European project ACLIMAS (EuropeAid/131046/C/ACT/Multi) and the Spanish MINECO project grant No. AGL2016-76527-R).
-Wheat
-Canopy temperature depression
-Grain yield
cc-by (c) Yousfi et al., 2019
https://creativecommons.org/licenses/by/4.0/
Artículo
Artículo - Versión publicada
MDPI
         

Documentos con el texto completo de este documento

Ficheros Tamaño Formato Vista
agronomy_a2019v9n6.pdf 1.992 MB application/pdf Vista/Abrir

Mostrar el registro completo del ítem

Documentos relacionados

Otros documentos del mismo autor/a